In machine vision, image enhancement techniques are used to process image data to facilitate operator and automated analysis. Commonly known image enhancement techniques can be divided into two broad classes: point transforms and neighborhood operations. Point transform algorithms are ones in which each output pixel is generated as a function of a corresponding input pixel. Neighborhood operations generate each output pixel as a function of several neighboring input pixels. Neighborhood size is often 3×3, 5×5, though it can be larger, smaller or shaped otherwise, in accord with requirements of a particular application.
Thresholding is an image enhancement technique for reducing the number of intensity, brightness or contrast levels in an image. It is typically used to convert a gray scale image, with up to 256 gray levels, to a binary image, with just two levels (e.g., black and white). If a pixel intensity value exceeds a threshold (or is outside a threshold range), it is converted to a value that represents “white” (or potential defect); otherwise, it is converted to a value that represents “black” (or “background”). Threshold levels can be set at a fixed gray level for an image (level thresholding), or can be based on a variety of other measures, e.g., they can be set relative to an average gray scale level for a region (base line thresholding).
Thresholding is commonly used in machine vision systems to facilitate detection of defects. Prior art techniques, however, do not perform very well on non-woven materials, such as disposable diaper fabrics. When these materials are backlit and image at high resolution, both embossing and normal variation in the material's “formation” (the structure of the material's fibers) can appear at the pixel level as small holes and/or thin spots amidst thicker, more solid regions. This makes it difficult to discern them from actual defects.
Traditionally, inspection system providers have solved the problem of inspecting such materials in one of two ways. They either image the materials at low camera resolutions, so minute variations in the materials are effectively lost, or they opto/mechanically defocus the camera lens, blurring the material variations to achieve somewhat the same effect. Both these techniques result in poor image quality and, therefore, cannot be used in applications where high-resolution images must be displayed, e.g., for operator evaluation. Moreover, both result in a loss of valuable image data at the acquisition stage and, therefore, preclude further automated analysis.
In view of the foregoing, an object of the invention is to provide improved methods and apparatus for machine vision. A more particular object is to provide improved methods and apparatus for thresholding images.
A related aspect of the invention is to provide such methods and apparatus as facilitate imaging and analysis of defects (or other features) in images.
A further aspect of the invention is to provide such methods and apparatus as facilitate the inspection of non-woven and other materials with high-frequency variations of intensity, brightness, color or contrast.
Yet another object of the invention is to provide such methods and apparatus as can be readily implemented at low cost with existing machine vision software and/or hardware.